Animals, including mice and rats, are used extensively as human models in research, for example the research of drug development, genetic functions, toxicology, and the understanding and treatment of diseases. Despite the differing lifestyles of humans and animals, their extensive genetic and neuro-anatomical similarities give rise to a wide variety of behavioral processes that are largely shared between species. Exploration of these shared brain functions sheds light on fundamental elements of human behavioral regulation.
Many behavioral test experiments have been designed for animals such as mice and rats to explore their behaviors. These experiments includes, but are not limited to, observation of home cage behaviors, open field locomotion experiments, object recognition experiments, maze experiments, freezing experiments for conditioned fear, gait analysis experiments, and monitoring of disease states such as cancer and epilepsy. In many cases these experiments use human observation of video of the experiment sessions, which often produces subjective and inaccurate results. Experiments using human observations are labor-intensive and thus expensive.
Over the last two decades major technological advances have enabled scientists to build a rich repository of rat and/or mouse models for epilepsy research. Scientists have generated numerous models of damage-induced epilepsy. In particular, systemic or focal-intercranial injection of substances such as kainic acid, tetanus toxin, pilocarpine, and other agents is known to cause a long-term or even permanent state of epileptic seizures. In these different models, the behaviors during the seizures have many similarities to human temporal lobe epilepsy, and furthermore, the electrophysical and histopathological abnormalities also have similarities.
These animal models are essential in the process of developing anti-epileptic drugs (AEDs). Currently, most researchers use animals from these models that exhibit seizures and either administer drugs or make other changes to the animal to see if the epileptic activity patterns are altered. This requires techniques that can measure the number, frequency, and types of occurring seizures.
Many current seizure detection systems involve using an electroencephalogram (EEG) for recording information, and using algorithms that can detect specific patterns in these EEG signals. Significant effort has been put into the research and development of EEG technology, and EEG signals can be accurately and automatically recorded. Improvements in EEG technology that have been made in recent years include radio telemetry technology that allows signals acquired by electrodes to be transmitted without tethers, and software that automatically analyzes EEG epileptiforms, determines the occurrences of seizures, and classifies the type of epilepsy.
However, using recorded EEG signals to detect seizures has important technical and conceptual problems. These problems include time-consuming and sophisticated surgery to implant electrodes into animals' brains, and the electrodes could have mechanical failures, causing significant alterations in the animals' behaviors or even sudden death of the animals. Noise in the EEG signals is also a drawback. Certain motor activity in an animal can cause severe noise in the EEG signals that needs to be filtered out. Sometimes, if this noise-causing activity is very similar to seizure activity, there will be no means of verifying occurrence of a seizure unless a video record is present.
When radio-telemetry of EEG signals is used, bandwidth becomes an issue as the sampling rate of the EEG signals that can be transmitted in limited to a few hundred hertz, compared to a standard rate of two kilohertz when using a tether. Another drawback with a radio-telemetry system is that multiple signals in the same room may interfere and lead to noisy data collection.
Finally, the software for detecting and classifying epileptic seizures in animals using EEG signals has not matured, with a precision of only fifty to sixty percent after twenty years of development. Due to this poor performance, many laboratories that have purchased this software have never used it for experiments. Analysis of EEG signals and epileptic activity is still largely done by manual observation, which is a slow, tedious, and highly-subjective process.
To a large extent, the approaches to seizure detection and classification have ignored another form of data—recorded video. While human observation of video images has its limitations, such as high subjectivity and high false-negative rates, progress in new technology such as digital video analysis suggests an alternative to EEG technology. A few video-EEG systems have been developed for both clinical use and research in animals. However, most of these video-EEG systems simply record both EEG signals and video for human review. The best these systems can do is display the EEG epileptiform and video images of the animal simultaneously on a screen while recording them for post-capture review. This is a subjective, time consuming, inefficient, and expensive process. It is likely that subtle seizures will be missed during rapid manual review of recorded video alone.